Aim To establish a model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia in China. Methods We retrospectively collected the medical records of 896 pregnant women with preterm preeclampsia who were older than 35 years and delivered at the Affiliated Hospital of Qingdao University from June 2018 to December 2020. The pregnant women were divided into an adverse outcome group and a non-adverse outcome group according to the occurrence of adverse outcomes. The data were divided into a training set and a verification set at a ratio of 8:2. A nomogram model was developed according to a binary logistic regression model created to predict the adverse outcomes in advanced-age pregnant women with preterm preeclampsia. ROC curves and their AUCs were used to evaluate the predictive ability of the model. The model was internally verified by using 1000 bootstrap samples, and a calibration diagram was drawn. Results Binary logistic regression analysis showed that platelet count (PLT), uric acid (UA), blood urea nitrogen (BUN), prothrombin time (PT), and lactate dehydrogenase (LDH) were the factors that independently influenced adverse outcomes (P < 0.05). The AUCs of the internal and external verification of the model were 0.788 (95% CI: 0.737 ~ 0.764) and 0.742 (95% CI: 0.565 ~ 0.847), respectively. The calibration curve was close to the diagonal. Conclusions The model we constructed can accurately predict the risk of adverse outcomes of pregnant women of advanced age with preterm preeclampsia, providing corresponding guidance and serving as a basis for preventing adverse outcomes and improving clinical treatment and maternal and infant prognosis.

Aim In this study, we established a model based on XGBoost to predict the risk of missed abortion in patients treated with in vitro fertilization-embryo transfer (IVF-ET), evaluated its prediction ability, and compared the model with the traditional logical regression model. Methods We retrospectively collected the clinical data of 1,017 infertile women treated with IVF-ET. The independent risk factors were screened by performing a univariate analysis and binary logistic regression analysis, and then, all cases were randomly divided into the training set and the test set in a 7:3 ratio for constructing and validating the model. We then constructed the prediction models by the traditional logical regression method and the XGBoost method and tested the prediction performance of the two models by resampling. Results The results of the binary logistic regression analysis showed that several factors, including the age of men and women, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anticardiolipin antibody (ACA), and thyroid peroxidase antibody (TPO-Ab), independently influenced missed abortion significantly (P < 0.05). The area under the receiver operating characteristic curve (AUC) score and the F1 score with the training set of the XGBoost model (0.877 ± 0.014 and 0.730 ± 0.019, respectively) were significantly higher than those of the logistic model (0.713 ± 0.013 and 0.568 ± 0.026, respectively). In the test set, the AUC and F1 scores of the XGBoost model (0.759 ± 0.023 and 0.566 ± 0.042, respectively) were also higher than those of the logistic model (0.695 ± 0.030 and 0.550 ± 049, respectively). Conclusions We established a prediction model based on the XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. This model performed better than the traditional logical regression model.

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Aim: To establish a model based on XGBoost to predict the risk of missed abortion in patients treated with in vitro fertilization-embryo transfer (IVF-ET), evaluate its prediction ability, and compare with the traditional logical regression model.Methods: The clinical data of 1017 infertile women treated with IVF-ET were collected retrospectively. The independent risk factors were screened by Univariate analysis and binary logistic regression analysis, and then all cases were randomly divided into training set and test set according to the proportion of 7:3 for model construction and verification evaluation. The prediction models are constructed by traditional logical regression method and XGBoost method respectively, and then the prediction performance of the two models is tested by resampling.Results: Binary logistic regression analysis showed that female age, male age, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anti cardiolipin antibody (ACA) and thyroid peroxidase antibody (TPO-Ab) were the factors that independently influenced missed abortion (P<0.05). The AUC score and F1 score with the training set of XGBoost model were 0.877±0.014 and 0.730±0.019. They were significantly higher than those of logistic model (0.713±0.013 and 0.568±0.026). In the evaluation of the test set, the AUC score and F1 score of XGBoost model were 0.759±0.023 and 0.566±0.042. They were also higher than those of logistic model (0.695±0.030 and 0.550±049).Conclusions: We established a prediction model based on XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. The performance of this model is better than the traditional logical regression model.